Media access control based user equipment data collection

Abstract

Example embodiments of the present disclosure relate to media access control (MAC)-based user equipment (UE) data collection. A method includes generating a media access control (MAC) protocol data unit (PDU) containing artificial intelligence (AI) data, wherein a header of the MAC PDU containing the AI data indicates at least one of: a presence of the AI data or a type of the AI data; generating a transport block (TB) including at least the MAC PDU containing the AI data; and transmitting the TB to a second apparatus.

Description

Technical Field

[0001] Various exemplary embodiments of this disclosure are generally related to the telecommunications field, and particularly to methods, apparatus, devices, and computer-readable storage media for collecting user equipment (UE) data based on Media Access Control (MAC). Background Technology

[0002] In some of the discussed scenarios, machine learning (ML) / artificial intelligence (AI) models are deployed at network devices. To train these models, data needs to be collected from the user experience (UE) and provided to the training function. Network operators require that the data used for training be visible to them. Summary of the Invention

[0003] In a first aspect of this disclosure, a first apparatus is provided. The first apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus to at least: generate a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data, wherein the header of the MAC PDU containing the AI ​​data indicates at least one of the following: the presence of AI data or the type of AI data; generate a Transport Block (TB), the TB including at least the MAC PDU containing the AI ​​data; and transmit the TB to a second apparatus.

[0004] In a second aspect of this disclosure, a second apparatus is provided. The second apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the second apparatus to at least: receive a TB from a first apparatus, the TB including at least a MAC PDU containing AI data; and retrieve AI data from the MAC PDU containing AI data, at least based on the header of the MAC PDU containing AI data.

[0005] In a third aspect of this disclosure, a method is provided. The method includes: generating a MAC PDU containing AI data by a first device, wherein the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data; generating a TB, wherein the TB includes at least the MAC PDU containing AI data; and transmitting the TB to a second device.

[0006] In a fourth aspect of this disclosure, a method is provided. The method includes: receiving a TB from a first device by a second device, the TB including at least a MAC PDU containing AI data; and obtaining AI data from the MAC PDU containing AI data, at least based on the header of the MAC PDU containing AI data.

[0007] In a fifth aspect of this disclosure, a first apparatus is provided. The first apparatus includes: components for generating a MAC PDU containing AI data, wherein the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data; components for generating a TB, wherein the TB includes at least the MAC PDU containing AI data; and components for transmitting the TB to a second apparatus.

[0008] In a sixth aspect of this disclosure, a second apparatus is provided. The second apparatus includes: components for receiving a TB from a first apparatus, the TB including at least a MAC PDU containing AI data; and components for retrieving AI data from the MAC PDU containing AI data, at least based on a header of the MAC PDU containing AI data.

[0009] In a seventh aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to a third aspect.

[0010] In an eighth aspect of this disclosure, a computer-readable medium is provided. The computer-readable medium includes instructions stored thereon for causing a device to perform at least the method according to the fourth aspect.

[0011] It should be understood that the summary portion is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] Some exemplary embodiments will now be described with reference to the accompanying drawings, in which:

[0013] Figure 1 The illustration shows an example communication environment in which example embodiments of the present disclosure may be implemented;

[0014] Figure 2 The illustration shows signaling diagrams of communications according to some example embodiments of the present disclosure;

[0015] Figure 3 The illustration shows an example diagram of a MAC-CE according to some example embodiments of the present disclosure;

[0016] Figure 4 Example diagrams of a TB according to some example embodiments of the present disclosure are illustrated;

[0017] Figures 5A to 5B The illustration shows an example diagram of a MAC sub-PDU according to some example embodiments of the present disclosure;

[0018] Figure 6The illustration shows signaling diagrams of communications according to some example embodiments of the present disclosure;

[0019] Figure 7 The illustration shows a flowchart of a method implemented at a first device according to some exemplary embodiments of the present disclosure;

[0020] Figure 8 The illustration shows a flowchart of a method implemented at a second device according to some exemplary embodiments of the present disclosure;

[0021] Figure 9 The illustration shows a flowchart of a method implemented at a first device according to some exemplary embodiments of the present disclosure;

[0022] Figure 10 The illustration shows a flowchart of a method implemented at a second device according to some exemplary embodiments of the present disclosure;

[0023] Figure 11 A simplified block diagram of a device suitable for implementing exemplary embodiments of the present disclosure is illustrated; and

[0024] Figure 12 A block diagram of an example computer-readable medium according to some example embodiments of the present disclosure is illustrated.

[0025] Throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements. Detailed Implementation

[0026] The principles of this disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are described for illustrative purposes only and to assist those skilled in the art in understanding and implementing this disclosure, and do not constitute any limitation on the scope of this disclosure. The embodiments described herein can be implemented in various ways other than those described below.

[0027] In the following description and claims, unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0028] In this disclosure, references to "an embodiment," "embodiment," and "example embodiment," etc., indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment must include that particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Moreover, when a particular feature, structure, or characteristic is described in connection with an embodiment, those skilled in the art will understand that, whether explicitly described or not, combining it with other embodiments to affect such a feature, structure, or characteristic is within the scope of their knowledge.

[0029] It should be understood that although the terms "first," "second," etc., preceding the nouns(s) herein may be used to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another and do not restrict the order of the nouns(s). For example, without departing from the scope of the exemplary embodiments, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element. As used herein, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0030] As used herein, “at least one of the following: ” and “at least one of the following: ” and similar wording (where the list of two or more elements is connected by “and” or “or”) means at least any one of these elements, or at least any two or more of these elements, or at least all of these elements.

[0031] As used herein, unless explicitly stated otherwise, “responding to A” does not mean that the step is performed immediately after “A” occurs, but rather that the step may include one or more intermediate steps.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. The singular forms “a,” “an,” and “the” used herein also include the plural forms unless the context clearly indicates otherwise. To be further understood, the terms “comprising,” “including,” “having,” “containing,” and / or “containing,” as used herein, specify the presence of the stated features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, and / or combinations thereof.

[0033] As used in this application, the term "circuit system" may refer to one or more or all of the following: (a) Pure hardware circuit implementation (such as implementations only in analog and / or digital circuit systems), and (b) A combination of hardware circuitry and software, such as (if applicable): (i) A combination of (multiple) analog and / or digital hardware circuits and software / firmware, and (ii) Any part of a hardware processor (including (multiple) digital signal processors), software, and (multiple) memories, which work together to enable a device (such as a mobile phone or server) to perform various functions, and (c) (Multiple) hardware circuits and / or (multiple) processors, such as (multiple) microprocessors or a portion thereof, which require software (e.g., firmware) to operate, but may be absent when operation is not required.

[0034] The definition of "circuit system" applies to all uses of the term in this application, including in any claim. As another example, as used in this application, the term "circuit system" also covers only hardware circuitry or a processor (or processors) or a portion of hardware circuitry or a processor and its accompanying software and / or firmware. For example, if applicable to a particular claim element, the term "circuit system" also covers baseband integrated circuits or processor integrated circuits for mobile devices, or similar integrated circuits in servers, cellular network devices, or other computing or network devices.

[0035] As used herein, the term "communication network" refers to a network that conforms to any suitable communication standard, such as New Radio (NR), Long Term Evolution (LTE), LTE-A Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), High-Speed ​​Packet Access (HSPA), Narrowband Internet of Things (NB-IoT), etc. Furthermore, communication between terminal devices and network devices in a communication network can be performed according to any suitable generation of communication protocol, including but not limited to first-generation (1G), second-generation (2G), 2.5G, 2.75G, third-generation (3G), fourth-generation (4G), 4.5G, fifth-generation (5G), 5.5G, sixth-generation (6G) communication protocols and / or any other protocols currently known or to be developed in the future. Embodiments of this disclosure can be applied to various communication systems. Given the rapid development of communications, there will naturally be communication technologies and systems that can be used to embody future types of communication technologies and systems. This should not be construed as limiting the scope of this disclosure to the systems described above.

[0036] As used herein, the term "network device" refers to a node in a communication network through which terminal devices access the network and receive services. A network device can refer to a base station (BS) or access point (AP), such as a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), an NR NB (also known as a gNB), a Remote Radio Unit (RRU), a Radio Header (RH), a Remote Radio Header (RRH), a relay, an Integrated Access and Backhaul (IAB) node, a low-power node (such as a femtosecond or picosecond), a non-terrestrial network (NTN), or a non-terrestrial network device (such as satellite network equipment, low Earth orbit (LEO) satellites and geostationary orbit (GEO) satellites, aircraft network equipment, etc.), depending on the terminology and technologies applied. In some example embodiments, the Radio Access Network (RAN) split architecture includes a centralized unit (CU) and a distributed unit (DU) at the IAB donor node. An IAB node includes a mobile terminal (IAB-MT) portion and a DU portion, where the IAB-MT portion behaves as a UE to the parent node, and the DU portion behaves as a base station to the next-hop IAB node.

[0037] The term "terminal device" refers to any terminal device capable of wireless communication. As an example and not a limitation, a terminal device may also be referred to as a communication device, user equipment (UE), subscriber station (SS), portable subscriber station, mobile station (MS), or access terminal (AT). Terminal devices can include, but are not limited to, mobile phones, cellular phones, smartphones, Voice over IP (VoIP) phones, wireless local loop phones, tablets, wearable terminal devices, personal digital assistants (PDAs), portable computers, desktop computers, image capture terminal devices (such as digital cameras), gaming terminal devices, music storage and playback devices, in-vehicle wireless terminal devices, wireless endpoints, mobile stations, laptop embedded devices (LEE), laptop mounted devices (LME), USB dongles, smart devices, wireless customer premises equipment (CPE), Internet of Things (IoT) devices, watches or other wearable devices, head-mounted displays (HMDs), vehicles, drones, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in industrial and / or automated processing chain environments), consumer electronics devices, devices operating on commercial and / or industrial wireless networks, etc. The terminal device may also correspond to the mobile terminal (MT) portion of an IAB node (e.g., a relay node). In the following description, the terms "terminal device," "communication device," "terminal," "user equipment," and "UE" are used interchangeably.

[0038] As used herein, the terms “resource,” “transmission resource,” “resource block,” “physical resource block” (PRB), “uplink resource,” or “downlink resource” can refer to any resource used to perform communication, such as communication between a terminal device and a network device, including time-domain resources, frequency-domain resources, spatial-domain resources, code-domain resources, or any other combination of time-domain resources, frequency-domain resources, spatial-domain resources, and / or code-domain resources used to implement communication. In the following, unless explicitly stated otherwise, resources in both the frequency and time domains will be used as examples of transmission resources to describe some exemplary embodiments of this disclosure. It should be noted that the exemplary embodiments of this disclosure are equally applicable to other resources in other domains.

[0039] Figure 1 An example communication network 100 in which exemplary embodiments of the present disclosure may be implemented is illustrated. Figure 1 As shown, the communication network 100 may include a first device 110, which may also be referred to as a terminal device or UE, for example.

[0040] The communication network 100 may also include a second device 120, which may be, for example, considered a network device or included in a network device. In some example embodiments, the network device may be discussed as a BS, gNB, or eNB.

[0041] The service area provided by the second device 120 is called a cell. The second device 120 can provide services to one or more cells of the first device 110. For example, the first device 110 can communicate with the second device 120 within cell 102. In some scenarios, cell 102 can be regarded as a cell serving the first device 110.

[0042] In the following description, for illustrative purposes, some exemplary embodiments are depicted in which the first device 110 operates as a terminal device and the second device 120 operates as a network device. However, in some exemplary embodiments, the operations described in connection with a terminal device may be implemented at a network device or other devices, and the operations described in connection with a network device may be implemented at a terminal device or other devices.

[0043] In some example embodiments, if the first device 110 is a terminal device and the second device 120 is a network device, the link from the second device 120 to the first device 110 is called a downlink (DL), and the link from the first device 110 to the second device 120 is called a UL. In the DL, the second device 120 is a transmission (TX) device (or transmitter), and the first device 110 is a reception (RX) device (or receiver). In the UL, the first device 110 is a TX device (or transmitter), and the second device 120 is an RX device (or receiver).

[0044] It should be understood that Figure 1 The number of devices and their connections shown are for illustrative purposes only and do not represent any limitation. Communication environment 100 may include any suitable number of devices configured to implement the exemplary embodiments of this disclosure. Although not shown, it should be understood that one or more additional devices may be located in cell 102, and one or more additional cells may be deployed in communication environment 100. It should be noted that although illustrated as a network device, network device 120 may be another device besides a network device. Although illustrated as a terminal device, terminal device 110 may be another device besides a terminal device.

[0045] In the following description, for illustrative purposes, some example embodiments are described in which terminal device 110 operates as a UE and network device 120 operates as a base station. However, in some example embodiments, the operations described in connection with the terminal device can be implemented at the network device or other devices, and the operations described in connection with the network device can be implemented at the terminal device or other devices.

[0046] Communication in communication environment 100 can be implemented according to any suitable communication protocol(s), including but not limited to cellular communication protocols, wireless local area network communication protocols such as IEEE 802.11, and / or any other protocols currently known or to be developed in the future. Furthermore, communication can utilize any suitable wireless communication technology, including but not limited to: Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Frequency Division Duplex (FDD), Time Division Duplex (TDD), Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), Discrete Fourier Transform Spread Spectrum OFDM (DFT-s-OFDM), and / or any other technology currently known or to be developed in the future.

[0047] In some of the discussed scenarios, numerous use cases were discussed where ML models needed to be executed only in the gNB, only in the UE, or half in the UE and half in the gNB. For models that will be executed (partially or entirely) in the UE, functionality is required to train these models. To train these models, relevant inference or training data needs to be collected at the UE and then transferred to the gNB for training.

[0048] In some other discussed schemes, data can be collected for the UE using various options to train a model that is expected to be used in the UE (so-called UE-side model and corresponding UE-side data collection). Based on some discussed schemes, options 2 and 3 of the following options have been prioritized for UE-side data collection: Option 1. The UE collects training data and transmits it directly to the over-the-top (OTT) server. 1a) OTT (Transparent) 1b) OTT (non-transparent) Option 2. The UE collects training data and transmits it to the core network (CN). The core network then transmits the training data to the OTT server. Option 3. The UE collects training data and transmits it to the Operation, Administration and Maintenance (OAM) system. The OAM system then transmits the necessary data to the OTT server.

[0049] Furthermore, some methods for UE-side data collection have been discussed in the proposed solutions. For example, regarding some service and system (SA) related solutions, from the RAN's perspective, data transmission on the user plane (UP) should be studied, and feedback on the satisfaction of visibility and controllability requirements should be provided.

[0050] In some of the proposed solutions, it has been concluded that data transmission via the control plane (CP) can satisfy the visibility and controllability requirements of the RAN, although some scalability issues have been raised for large-scale data transmission.

[0051] On the one hand, there is no discussion about the details of UP, namely how to differentiate it according to the requirements related to ML / AI inference data.

[0052] On the other hand, some solutions related to the new design in the RAN required for 6G have been discussed. The new design for a unified technical solution for multiple use cases targeting UP is as follows: • Avoid data duplication for different use cases (sensing, ML / AI, self-optimizing network (SON) / minimum drive test (MDT), etc.). • Efficient transmission of large amounts of internal data. • Different termination points for data access (RAN, CN, UE, Application Function (AF)).

[0053] Regarding the support provided for the data plane in RAN, here are some alternatives: • Alternative 1: New Signal Resource Bearer (SRB) for data collection between UE and RAN, and Data Radio Bearer (DRB) for data collection between UE and CN. • Alternative 2: A new radio bearer (RB) suitable for all situations. • gNB management was introduced for data collection between the UE and gNB, for data quality and RB management.

[0054] Based on the above discussion, data collection from the UE is an important aspect under discussion, and transmitting data from the UE to the network via the UP is a strong candidate for data collection from the UE. Data collection from the UE is for ML / AI model inference at the gNB. The amount of inference data is typically small, and UP transmission is more suitable for this purpose.

[0055] In some of the discussed scenarios, use cases are described where model training can be performed at the OAM (Operational Information Center), and the model can be transferred to the gNB (Gate NB) for inference. For these use cases, the amount of inference data collected from the UE is small. Typical use cases involve AI inference for beam management, Channel State Information (CSI) compression, etc. The latency requirements for such inference data are very stringent. A small amount of data needs to be rapidly transmitted to the gNB for inference so that decisions from the AI ​​model can be implemented for the UE.

[0056] As mentioned above, the collection of inference data requires certain conditions, as follows: • Latency-sensitive requirements – very stringent latency requirements. • Smaller data volume – The amount of typical inference data required is relatively small.

[0057] In some discussed scenarios, a UP tunnel is established between the UE and the User Plane Function (UPF). The tunnel is terminated at the UPF. Data of interest to the gNB in ​​the Protocol Data Unit (PDU) session is transparently transmitted through the gNB. The gNB cannot consume the data required for ML / AI inference via the PDU session. Data terminated at the UPF needs to be transmitted back to the gNB for inference, resulting in greater latency and inefficient resource utilization. In this case, technical solutions involving multi-layer protocol stack processing increase latency, especially considering decomposed architectures (e.g., gNBs based on Central Unit-Distributed Unit (CU-DU) architectures).

[0058] Inference data collection from the UE will be a key factor for inference at the gNB using ML / AI models, especially given the many more use cases being discussed in 6G and beyond.

[0059] Therefore, a technical solution is needed to efficiently collect inference data at the gNB without significant latency. Furthermore, the gNB also requires a technical solution for identifying and collecting inference data from the UE for ML / AI inference purposes at the gNB, and a technical solution for transferring small AI models from the UE to the gNB.

[0060] According to some example embodiments of this disclosure, a technical solution for MAC-based UE data collection is provided. In this technical solution, a first device 110 generates a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data. The header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data. The first device 110 generates a Transport Block (TB), which at least includes the MAC PDU containing AI data. The first device 110 transmits the TB to a second device 120.

[0061] As described above, the technical solution proposed in this disclosure enables the first device 110 (or UE) to apply a new priority proposed for AI data when constructing a new MACPDU, and to apply a configuration indicated by the second device 120 (or gNB) to package the AI ​​data MAC PDU with other MAC PDUs and Media Access Control-Control Element (MAC CE), so that the first device 110 can package TB with AI data.

[0062] The technical solution proposed in this disclosure also introduces a new DL MAC-CE, enabling the second device 120 to request the type of AI data (e.g., inference data, small ML model) from the first device 110. Furthermore, the DL MAC-CE can also instruct how to package AI data-related MAC PDUs with ordinary MAC PDUs to create a TB.

[0063] In this way, the first device 110 can transmit AI data to the second device by enhancing the MAC PDU, which improves the efficiency of AI data transmission without requiring major modifications to the current TB and MAC PDU structure.

[0064] Furthermore, the proposed new DL MAC-CE provides an agile and efficient way for the second device 120 to request the AI ​​data required for services running at the second device 120.

[0065] The exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0066] Now for reference Figure 2 This illustrates signaling diagram 200 for communication according to some example embodiments of the present disclosure. Figure 2 As shown, signaling diagram 200 relates to first device 110 and second device 120. For discussion purposes, reference is made to... Figure 1 To describe signaling diagram 200.

[0067] like Figure 2As shown, the first device 110 can transmit (220) information indicating the first device 110's ability to transmit AI data via a MAC PDU. It should be noted that the MAC PDU mentioned herein may also be referred to as or include a MAC subprotocol data unit (subPDU) with a MAC subheading.

[0068] In some embodiments, capability information may be indicated in an information element (IE), such as " AIDataTransferParameters The IE browser includes the field " MACPduTransferCapable::=BOOLEAN ".

[0069] For example, the first device 110 may indicate to the second device 120 whether it can support preparing AI data directly as a MAC PDU and making it available when the Transport Block (TB) at the MAC layer is prepared. The TB carrying the AI ​​data may be transmitted to the second device. The TB may include a MAC PDU containing the AI ​​data. In some embodiments, the TB may only include a MAC PDU containing the AI ​​data.

[0070] In addition, a TB may include at least one additional MAC PDU that does not contain AI data. In this case, the TB may include both a MAC PDU containing AI data and a MAC PDU not containing AI data (i.e., a normal MAC PDU). It should be noted that, in the following text, a MAC PDU containing AI data may also be referred to as a MAC PDU.

[0071] In some embodiments, if capability information indicates that the first device 110 can transmit AI data via a MAC PDU containing AI data, the second device 120 can transmit a (225) Radio Resource Control (RRC) message indicating that the first device 110 collects AI data via a MAC PDU containing AI data. For example, the RRC message could be an enhanced RRC reconfiguration message that enables the second device 120 to instruct the first device 110 to collect AI data via a MAC PDU.

[0072] In some embodiments, the RRC message can also indicate the period of AI data transmission. For example, AI data collected via the MAC PDU can also be configured to be collected periodically based on the period indicated by the RRC message (or RRC configuration or RRC reconfiguration).

[0073] like Figure 2 As shown, the second device 120 can transmit (230) a request for AI data to the first device 110. For example, the request can at least instruct the first device to provide a specific type of AI data.

[0074] In some embodiments, the request may be transmitted to the first device 110 via a MAC-CE. The MAC-CE mentioned herein may also be referred to as a DL MAC-CE. The MAC-CE of AI data can be identified by a MAC subheader having a Logical Channel Identifier (LCID).

[0075] In some embodiments, the new MAC-CE can indicate the cycle of AI data transmission.

[0076] In some embodiments, the MAC CE can be enhanced to indicate the period of transmission of periodic AI data. The period of AI data transmission can be changed frequently to accommodate the ever-changing data requirements at the second device 120.

[0077] In some embodiments, when the RRC configuration is used to configure the cycle (as described above), the MAC-CE can be used solely to activate the periodic transmission of AI data. For example, the new MAC-CE can act as a message instructing the transmission of AI data, wherein later, the first device 110 will prepare the periodic AI data (based on the RRC configuration) and buffer it in the MAC layer for use in the ongoing UL transmission.

[0078] As another embodiment, when parameters (e.g., AI data type and reporting period) are configured in the RRC message, downlink control information (DCI) can be used to activate and / or deactivate the reporting of AI data.

[0079] In some embodiments, the request may also (e.g., via MAC-CE) indicate the type of header to be used in the MAC PDU containing AI data. For example, the request may indicate how the type of AI data is recorded in the header of the MAC PDU containing AI data (described later in conjunction with Figure 5).

[0080] In some embodiments, the MAC-CE may have a fixed size of 2 octets, with the first octet indicating the type of AI data requested. For example, the request may indicate the presence of a bitmap or unique number in the header of the MAC PDU containing the AI ​​data (described later in conjunction with Figure 5).

[0081] In some embodiments, the MAC-CE associated with the request may have a new structure suitable for the specifications in 6G.

[0082] like Figure 3 The diagram illustrates a potential embodiment of the new MAC-CE. It should be noted that the exact details and packaging of the new MAC-CE may vary. The structure of the new MAC-CE is merely an example.

[0083] In some embodiments, as shown in field 320 (i.e., the first octet), the MAC-CE may contain the type of AI data requested by the second device 120. Furthermore, as shown in field 310, the MAC-CE may also include information indicated by the second device 120 regarding the placement of the AI ​​data in the TB. For example, the first two bits of the second octet (i.e., field 310) indicate which option will be used to prioritize and place the MAC PDU containing the AI ​​data in the TB. The remaining six bits of the second octet are reserved (i.e., field 330).

[0084] The placement of AI data can be referred to as the location or pattern (e.g., priority) used to place the MAC PDU containing the AI ​​data within the TB.

[0085] Upon receiving the request, the first device may acquire a pattern for placing a MAC PDU containing AI data relative to at least one other MAC PDU.

[0086] In some embodiments, the pattern or location for placing the MAC PDU containing AI data can be configured by the second device 120. For example, the pattern can be obtained from a request instructing the first device to provide a specific type of AI data via MAC-CE.

[0087] In some other embodiments, the first device 110 may also be configured with a default mode for placing a MAC PDU during existing bearer configuration information. The second device 120 may also override the default mode with a new MAC-CE in the DL MAC-CE as described above, which is used to request the first device 110 to transmit AI data in the form of a MAC PDU in the UL.

[0088] Refer again Figure 2 Based on the mode or default mode indicated by the second device 120, the first device 110 can determine the location of the MAC PDU containing AI data in the TB and generate (235) TB, the TB including at least the MAC PDU containing AI data and at least one other MAC PDU. For example, the first device 110 can prepare the AI ​​data-related MAC PDU based on the configuration from the second device 120 (e.g., the configuration may be indicated by an RRC message or request).

[0089] like Figure 4As shown in the diagram, an example diagram of a TB is presented. The TB includes a MAC sub-PDU containing AI data (also referred to as a MAC PDU) and other MAC PDUs that do not contain AI data (also referred to as other MAC PDUs). When preparing the TB, the first device 110 needs to determine the location or position of the MAC PDU containing AI data.

[0090] In some embodiments, the priority (or location or mode) of such a MAC PDU containing AI data can be specified so that the first device 110 can follow a standardized priority and prepare a TB including the MAC PDU containing AI data accordingly.

[0091] In some embodiments, as shown at box 410, the pattern may include a position in the TB preceding at least one additional MAC PDU. In other words, the MAC PDU containing AI data may be arranged in the TB as the first (highest priority) preceding the normal PDU, followed by the normal PDU, then the MAC CE (e.g., the PDU shown in box 420), and then optionally the padding PDU.

[0092] In some embodiments, the pattern may include a location between at least one additional MAC PDU within the TB. In other words, the PDU containing AI data may be arranged between the normal PDU and the MAC CE in the TB formation. For example, as... Figure 4 As shown, the MAC PDU containing AI data can be placed at position 430.

[0093] In some embodiments, the pattern may include a position within the TB following at least one additional MAC PDU. In other words, a new PDU with AI data may be positioned after the regular PDU and MAC CE in the TB formation. For example, a MAC PDU containing AI data may be placed at position 440.

[0094] In some embodiments, the header of the MAC PDU associated with AI data may be enhanced to include that type of AI data. For example, the header of a MAC PDU containing AI data may indicate at least one of the following: the presence of AI data or the type of AI data.

[0095] In some embodiments, in order to indicate that AI data exists in a certain MAC PDU, a new LCID can be assigned to indicate that the data contained in the new LCID contains AI data.

[0096] MAC PDUs containing the AI ​​data described in this article can have new structures to carry AI-related information (e.g., the type of AI data).

[0097] Now for reference Figure 5A It shows an example diagram of a sample structure for a MAC PDU containing AI data.

[0098] like Figure 5A As shown, in addition to AI data 530, the MAC PDU containing AI data may also include a header 520. Different implementations can be used for the header 520 of the new MAC PDU to indicate the type of AI data.

[0099] As shown in box 510, a MAC PDU containing AI data may include AI data 530 and a header 520. The header includes information related to the type of AI data and other information, such as LCID. The structure of the MAC PDU shown in box 510 can be adapted to scenarios where multiple types of AI data can be transmitted via TB, and the header can indicate the type of AI data in a specific MAC sub-PDU containing AI data (e.g., AI data 530).

[0100] In this case, if multiple (or different types) of AI data are transmitted, the header 520 of the MAC PDU can also be updated with information indicating the type of AI data, in addition to the new Logical Channel Identifier (LCID).

[0101] In some examples, as an option, the first device may determine a bitmap indicating whether AI data (e.g., inference data) is included in the MAC PDU and the type of AI data. For example, in addition to a fixed LCID (e.g., the LCID indicated at header 520), if there are potentially different types of AI data and it may be necessary to differentiate the MAC PDU based on the type of AI data, the header 520 of the MAC PDU may be enhanced to indicate the type of AI data in the header 520. In this example, header 520 may include a bitmap indicating the type of AI data.

[0102] For example, a bitmap may include a first bit (e.g., bit "0") corresponding to inference data, which could be AI data. When this bit is set to "1", the inference data is included in the MAC PDU. Conversely, if the bit is set to "0", the inference data is not included.

[0103] As another example, the bitmap may also include a second bit (e.g., bit "1") corresponding to the small ML model data, which can also be a type of AI data. When set to "1", it indicates that the small ML model data exists in the MAC PDU.

[0104] After determining the bitmap, the first device 110 can generate a header 520 of a MAC PDU containing AI data based on the bitmap.

[0105] In some other examples, as an alternative, the first device 110 can determine a unique number or unique enhanced logical channel identifier (eLCID) assigned to the type of AI data included in the MAC PDU containing the AI ​​data. For example, a unique number can be assigned to different types of AI data and ML models, and this unique number can be used in an introduced new octet to distinguish different types of AI data. In another embodiment, an extended eLCID (included in the header 520) can also be used for this purpose, in which case a unique eLCID can be assigned to different types of AI data.

[0106] After determining the unique number or unique eLCID, the first device 110 can generate a header 520 of a MAC PDU containing AI data based on the unique number or unique eLCID.

[0107] Now for reference Figure 5B This shows another example diagram of a sample structure for a MAC PDU containing AI data.

[0108] like Figure 5B As shown in the diagram, box 540 illustrates another structure for the MAC PDU header, which can be applied to scenarios where only one type of AI data is transmitted via TB.

[0109] In this case, if only one type of AI data is transmitted to the second device 120, the new (or fixed) LCID 560 can be used to identify the AI ​​data.

[0110] In some other examples, as an alternative, if only one type of AI data is possible, the first device 110 can generate the header of the MAC PDU based on a fixed LCID 560 assigned to a predetermined type of AI data (e.g., the type of AI data indicated at field 550). For example, for a MAC PDU containing AI data, the LCID 560 can be fixed, and such a PDU is identified with a fixed LCID. This is feasible when the second device 120 requests and the first device 110 provides only one type of AI data. In this case, if only one type of AI data is transmitted, the new LCID 560 may be sufficient for the second device 120 to identify the AI ​​data in the TB.

[0111] Based on the above header generation process, the first device 110 can generate a MACPDU containing AI data with a corresponding header, which indicates at least one of the following: the presence of AI data or the type of AI data. For example, the length of the AI ​​data can be indicated at field 570.

[0112] After generating the requested MAC PDU containing AI data, the first device 110 may also determine the position of the MAC PDU containing AI data in the TB relative to at least one other MAC PDU that does not contain AI data (e.g., based on a configured default mode, or as described above). Figure 4 The mode configured by MAC-CE (as described).

[0113] In this way, the first device 110 can prepare a MAC PDU with AI data and use information indicating that the MAC PDU consists of AI data to properly construct the header.

[0114] Through the process of constructing the MAC PDU header described above, the first device 110 does not need to be configured with any specific logical channel configuration to prepare such a MAC PDU. The first device 110 can use an existing PDU session, and the MAC PDU can be prepared according to the existing logical channel configuration. The new MAC PDU created by the first device 110 can be available during the TB preparation of the MAC layer of the first device 110.

[0115] In this way, less configuration is required when using MAC PDU to transmit AI data, which improves transmission efficiency and throughput.

[0116] Refer again Figure 2 The first device 110 can generate a TB, which includes at least a MAC PDU containing AI data. The TB may also include at least one additional MAC PDU that does not contain AI data. Based on the generated TB, the first device 110 can transmit (240) TB to the second device 120.

[0117] Upon receiving the TB, the second device may obtain (245) AI data from the MAC PDU containing AI data, at least based on the header of the MAC PDU containing AI data.

[0118] In some embodiments, the second device 120 may determine at least one of the presence or type of AI data based on the header of the MAC PDU. If the second device 120 determines that AI data exists, it may acquire AI data of that type.

[0119] The second device 120 can obtain AI data by decoding the information stored in the header of the MAC PDU containing AI data.

[0120] In some embodiments, the second device 120 may determine whether AI data (e.g., inference data) is included in the MAC PDU and the type of AI data based on the bitmap included in the header. If the second device 120 determines that the AI ​​data is included in the MAC PDU, the second device 120 may acquire the AI ​​data associated with the type of AI data.

[0121] In some other embodiments, the second device 120 can determine the type of AI data contained in the MAC PDU based on a unique number or unique eLCID assigned to the type of AI data contained in the MAC PDU. The second device 120 can then acquire the AI ​​data of that type.

[0122] In some other embodiments, the second device 120 can acquire AI data corresponding to a predetermined type based on a fixed LCID assigned to AI data of a predetermined type.

[0123] In this way, an efficient process for transmitting AI data to the second device 120 is achieved, which reduces the latency of the AI ​​data payload and improves system responsiveness.

[0124] Now for reference Figure 6 This illustrates a signaling diagram 600 for communication according to some example embodiments of the present disclosure. For example... Figure 6 As shown, signaling diagram 600 relates to first device 110 and second device 120. For discussion purposes, reference is made to... Figure 1 To describe signaling diagram 600.

[0125] Figure 6 A more detailed process for AI data transmission is shown.

[0126] like Figure 6 As shown, at block 605, the first device 110 is connected to the network (i.e., the second device 120) and is in RRC connection mode, establishing a bearer.

[0127] During connection, the first device 110 may indicate (610) its capabilities, which may also include information about whether the first device 110 is able to support AI data transmission via MAC PDU.

[0128] Based on the capabilities of the first device 110, the second device 120 may make a decision (615) based on the capabilities of the first device 110, as well as other factors including whether it is necessary to collect AI data from the first device 110 for inference (or for other purposes).

[0129] If the second device 120 determines that data needs to be collected by the first device 110, the second device 120 may transmit (620) an instruction to reconfigure the RRC of the AI ​​data collected via the MAC.

[0130] After receiving the RRC message and performing the corresponding reconfiguration, the first device 110 can transmit the (625) RRC reconfiguration complete message to the second device 120.

[0131] When data needs to be collected from the first device 110 at the MAC level for AI inference or other purposes, the second device 120 can prepare a new MAC-CE with additional information.

[0132] In some embodiments, the additional information may include the type of AI data requested by the second device 120. The additional information may also include the order (or priority, pattern, or location) in which the MAC PDU containing the AI ​​data needs to be packaged when forming a TB.

[0133] In addition, the additional information may include the type (or kind) of header to be used in the MAC sub-PDU (also known as MACPDU) containing AI data. For example, the type (or kind) of header to be used may include a MAC PDU header, which includes a bitmap or unique number / eLCID or fixed LCID, etc.

[0134] After the new MAC-CE is prepared, the second device can transmit (630) a MAC-CE indicating a request for AI data to the first device 110.

[0135] Based on the request, the first device 110 can construct (635) a new MAC PDU containing AI data.

[0136] like Figure 6 As shown, the first device 110 can generate (640) MAC PDUs, and the MAC header of the MAC PDU containing AI data can also be updated.

[0137] The generation of the MAC PDU (including the header) can include several different options, which are selected based on the type of AI data that the first device 110 requests to be measured and transmitted. These options have already been described above and will not be repeated here.

[0138] Based on the new priority configured for AI data and the placement instructions of the MAC PDU containing AI data indicated by the second device 120 in the new MAC-CE, the first device 110 can form (645) a new MAC PDU containing AI data placed in the TB. Based on the formed new MAC PDU, the first device 110 can generate the TB, which includes the new MAC PDU containing AI data and other MAC PDUs that do not contain AI data.

[0139] like Figure 6 As shown, the first device 110 can transmit (650) a TB having a new MAC PDU containing AI data.

[0140] Upon receiving the TB, the second device 120 can decode the header of the (655) MAC PDU and identify the AI ​​data.

[0141] Based on the identified AI data, the second device 120 can (660) consume the AI ​​data. For example, the second device 120 can also appropriately use the AI ​​data for reasoning purposes.

[0142] Through the above process, the first device 110 can provide the second device 120 with the required AI data, which reduces the response time of AI data transmission and improves the success rate of AI data transmission.

[0143] Furthermore, the various technical solutions for MAC PDUs described in this article are also applicable to MAC sub-PDUs, and vice versa. Similarly, the technical solutions for headers described in this article are also applicable to sub-headers, and vice versa.

[0144] Figure 7 A flowchart of an example method 700 implemented at a first device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 1 The angle description method 700 of the first device 110 in the middle.

[0145] At box 710, the first device 110 generates a MAC PDU containing AI data, wherein the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data.

[0146] At frame 720, the first device 110 generates a TB, which includes at least a MAC PDU containing AI data.

[0147] At frame 730, the first device 110 transmits TB to the second device.

[0148] In some example embodiments, method 700 further includes: determining a bitmap, the bitmap indicating whether AI data is included in a MAC PDU containing AI data and the type of AI data; and generating a header of the MAC PDU containing AI data based on the bitmap.

[0149] In some example embodiments, method 700 further includes: determining a unique number or unique eLCID assigned to the type of AI data included in the MAC PDU containing AI data; and generating a header of the MAC PDU containing AI data based on the unique number or unique eLCID.

[0150] In some example embodiments, method 700 further includes generating a header of a MAC PDU containing AI data based on a fixed LCID assigned to AI data of a predetermined type.

[0151] In some example embodiments, method 700 further includes: determining the position of the MAC PDU containing AI data in the TB relative to at least one other MAC PDU that does not contain AI data.

[0152] In some example embodiments, method 700 further includes: obtaining a pattern for placing the MAC PDU containing AI data relative to at least one other MAC PDU; and determining the location of the MAC PDU containing AI data in the TB based on the pattern.

[0153] In some example embodiments, the pattern includes at least one of the following: a position in the TB preceding at least one additional MAC PDU, a position in the TB between at least one additional MAC PDU, or a position in the TB following at least one additional MAC PDU.

[0154] In some example embodiments, the mode is obtained from a request or based on a default mode, which instructs the first device to provide a specific type of AI data via a Media Access Control-Control Element (MAC-CE).

[0155] In some example embodiments, method 700 further includes transmitting capability information to a second device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0156] In some example embodiments, method 700 further includes: receiving a request from a second device via a MAC-CE that at least instructs the first device to provide a specific type of AI data; and generating a MAC PDU containing the AI ​​data based on the request.

[0157] In some example embodiments, the request also indicates the period of AI data transmission.

[0158] In some example embodiments, method 700 further includes receiving an RRC message from a second device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0159] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0160] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0161] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0162] Figure 8 A flowchart of an example method 800 implemented at a second device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 1 The second device 120 in the method of angle description 800.

[0163] At frame 810, the second device 120 receives a TB from the first device, the TB including at least a MAC PDU containing AI data.

[0164] At frame 820, the second device 120 acquires AI data from the MAC PDU containing AI data, based at least on the header of the MAC PDU containing AI data.

[0165] In some example embodiments, method 800 further includes: determining the presence and type of AI data based on the header of the MAC PDU containing AI data; and obtaining AI data of that type based on the determination that the AI ​​data exists.

[0166] In some example embodiments, method 800 further includes: determining, based on the bitmap included in the header, whether AI data is included in a MAC PDU containing AI data and the type of AI data; and, based on the determination that AI data is included in a MAC PDU containing AI data, obtaining AI data associated with the type of AI data.

[0167] In some example embodiments, method 800 further includes: determining the type of AI data contained in the MAC PDU containing AI data based on a unique number or unique eLCID assigned to the type of AI data contained in the MAC PDU containing AI data; and obtaining the AI ​​data of that type.

[0168] In some example embodiments, method 800 further includes: obtaining AI data corresponding to the predetermined type based on a fixed LCID of the AI ​​data assigned to the predetermined type.

[0169] In some example embodiments, method 800 further includes: receiving capability information from a first device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0170] In some example embodiments, method 800 further includes transmitting a request to the first device via a Media Access Control-Control Element (MAC-CE) to at least instruct the first device to provide a specific type of AI data.

[0171] In some example embodiments, the request also indicates the period of AI data transmission.

[0172] In some example embodiments, method 800 further includes transmitting an RRC message to a first device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0173] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0174] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0175] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0176] Figure 9 A flowchart of an example method 900 implemented at a first device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 1 The angle description method of the first device 110 in the middle is 900.

[0177] At frame 910, the first device 110 receives a request for AI data from the second device.

[0178] At frame 920, the first device 110 generates a TB based at least on the request, the TB including at least a MACPDU containing AI data.

[0179] At frame 930, the first device 110 transmits TB to the second device.

[0180] In some example embodiments, method 900 further includes: receiving a request from a second device via a media access control-control element (MAC-CE) that at least instructs the first device to provide a specific type of AI data; and generating a MAC PDU containing the AI ​​data based on the request.

[0181] In some example embodiments, the request also indicates the period of AI data transmission.

[0182] In some example embodiments, the request also indicates the type of header to be used in the MAC PDU containing AI data.

[0183] In some example embodiments, method 900 further includes: receiving an RRC message from a second device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0184] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0185] In some example embodiments, the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data.

[0186] In some example embodiments, method 900 further includes: determining a bitmap indicating whether AI data is included in a MAC PDU containing AI data and the type of AI data; and generating a header of the MAC PDU containing AI data based on the bitmap.

[0187] In some example embodiments, method 900 further includes: determining a unique number or unique eLCID assigned to the type of AI data included in the MAC PDU containing AI data; and generating a header of the MAC PDU containing AI data based on the unique number or unique eLCID.

[0188] In some example embodiments, method 900 further includes generating a header of a MAC PDU containing AI data based on a fixed LCID assigned to AI data of a predetermined type.

[0189] In some example embodiments, method 900 further includes transmitting capability information to a second device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0190] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0191] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0192] Figure 10 A flowchart of an example method 1000 implemented at a second device according to some example embodiments of the present disclosure is shown. For the purposes of discussion, [the following will be discussed]. Figure 1 The angle description method 1000 of the second device 120 in the middle.

[0193] At frame 1010, the second device 120 transmits a request for AI data to the first device.

[0194] At frame 1020, the second device 120 receives a TB from the first device, the TB including at least a MAC PDU containing AI data.

[0195] At frame 1030, the second device 120 acquires AI data from the MAC PDU containing AI data, based at least on the header of the MAC PDU containing AI data.

[0196] In some example embodiments, method 1000 further includes transmitting a request to the first device via MAC-CE that at least instructs the first device to provide a specific type of AI data.

[0197] In some example embodiments, the request also indicates the period of AI data transmission.

[0198] In some example embodiments, the request also indicates the type of header to be used for the MAC PDU containing AI data.

[0199] In some example embodiments, method 1000 further includes transmitting an RRC message to a first device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0200] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0201] In some example embodiments, the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data.

[0202] In some example embodiments, method 1000 further includes: determining the presence and type of AI data based on the header of the MAC PDU containing AI data; and obtaining AI data of that type based on the determination that the AI ​​data exists.

[0203] In some example embodiments, method 1000 further includes: determining, based on the bitmap included in the header, whether AI data is included in a MAC PDU containing AI data and the type of AI data; and, based on the determination that AI data is included in a MAC PDU containing AI data, obtaining AI data associated with the type of AI data.

[0204] In some example embodiments, method 1000 further includes: determining the type of AI data contained in the MAC PDU containing AI data based on a unique number or unique eLCID assigned to the type of AI data contained in the MAC PDU containing AI data; and obtaining the AI ​​data of that type.

[0205] In some example embodiments, method 1000 further includes: obtaining AI data corresponding to the predetermined type based on a fixed LCID of the AI ​​data assigned to the predetermined type.

[0206] In some example embodiments, method 1000 further includes: receiving capability information from a first device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0207] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0208] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0209] In some example embodiments, a first device capable of performing any of method 700 (e.g., Figure 1 The first device 110 may include a component for performing the corresponding operation of method 700. This component may be implemented in any suitable form. For example, the component may be implemented in a circuit system or a software module. The first device may be implemented as... Figure 1 The first device 110 may be included therein.

[0210] In some example embodiments, the first device includes components for generating a MAC PDU containing AI data, wherein the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data; components for generating a TB, wherein the TB includes at least the MAC PDU containing AI data; and components for transmitting the TB to a second device.

[0211] In some example embodiments, the first apparatus further includes: a component for determining a bitmap, the bitmap indicating whether AI data is included in a MAC PDU containing AI data and the type of AI data; and a component for generating a header of a MAC PDU containing AI data based on the bitmap.

[0212] In some example embodiments, the first apparatus further includes: a component for determining a unique number or unique eLCID of the type of AI data assigned to the MAC PDU containing AI data; and a component for generating a header of the MAC PDU containing AI data based on the unique number or unique eLCID.

[0213] In some example embodiments, the first apparatus further includes a component for generating a header of a MAC PDU containing AI data based on a fixed LCID assigned to AI data of a predetermined type.

[0214] In some example embodiments, the first device further includes a component for determining the position of the MAC PDU containing AI data in the TB relative to at least one other MAC PDU that does not contain AI data.

[0215] In some example embodiments, the first apparatus further includes: components for acquiring a pattern for placing a MAC PDU containing AI data relative to at least one other MAC PDU; and components for determining the location of the MAC PDU containing AI data in the TB based on the pattern.

[0216] In some example embodiments, the pattern includes at least one of the following: a position in the TB preceding at least one additional MAC PDU, a position in the TB between at least one additional MAC PDU, or a position in the TB following at least one additional MAC PDU.

[0217] In some example embodiments, the mode is obtained from a request or based on a default mode, which instructs the first device E to provide a specific type of AI data via MAC-C.

[0218] In some example embodiments, the first device further includes a component for transmitting capability information to the second device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0219] In some example embodiments, the first device further includes: components for receiving, via MAC-CE, a request from the second device that at least instructs the first device to provide a specific type of AI data; and components for generating a MAC PDU containing the AI ​​data based on the request.

[0220] In some example embodiments, the request also indicates the period of AI data transmission.

[0221] In some example embodiments, the first device further includes a component for receiving an RRC message from the second device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0222] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0223] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0224] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0225] In some example embodiments, a second means capable of performing any of method 800 (e.g., Figure 1 The second device 120 may include a component for performing the corresponding operation of method 800. This component can be implemented in any suitable form. For example, the component can be implemented in a circuit system or a software module. The second device can be implemented as... Figure 1 The second device 120 may be included therein.

[0226] In some example embodiments, the second device includes: a component for receiving a TB from the first device, the TB including at least a MAC PDU containing AI data; and a component for obtaining AI data from the MAC PDU containing AI data based at least on the header of the MAC PDU containing AI data.

[0227] In some example embodiments, the second apparatus further includes: components for determining the presence and type of AI data based on the header of the MAC PDU containing AI data; and components for acquiring AI data of that type based on the determination that the AI ​​data is present.

[0228] In some example embodiments, the second apparatus further includes: components for determining whether AI data is included in a MAC PDU containing AI data and the type of AI data based on a bitmap included in the header; and components for obtaining AI data associated with the type of AI data based on the determination that AI data is included in a MAC PDU containing AI data.

[0229] In some example embodiments, the second apparatus further includes: a component for determining the type of AI data contained in the MAC PDU containing AI data based on a unique number or unique eLCID assigned to the type of AI data contained in the MAC PDU containing AI data; and a component for acquiring the AI ​​data of that type.

[0230] In some example embodiments, the second apparatus further includes a component for acquiring AI data corresponding to a predetermined type based on a fixed LCID assigned to AI data of a predetermined type.

[0231] In some example embodiments, the second device further includes a component for receiving capability information from the first device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0232] In some example embodiments, the second device further includes a component for transmitting a request via MAC-CE to the first device that at least instructs the first device to provide a specific type of AI data.

[0233] In some example embodiments, the request also indicates the period of AI data transmission.

[0234] In some example embodiments, the second device further includes a component for transmitting an RRC message to the first device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0235] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0236] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0237] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0238] In some example embodiments, a first device capable of performing any of the methods 900 (e.g., Figure 1 The first device 110 may include a component for performing the corresponding operation of method 900. This component may be implemented in any suitable form. For example, the component may be implemented in a circuit system or a software module. The first device may be implemented as... Figure 1 The first device 110 may be included therein.

[0239] In some example embodiments, the first device includes: components for receiving a request for AI data from a second device; components for generating a TB based at least on the request, the TB including at least a MAC PDU containing the AI ​​data; and components for transmitting the TB to the second device.

[0240] In some example embodiments, the first device further includes: components for receiving, via MAC-CE, a request from the second device that at least instructs the first device to provide a specific type of AI data; and components for generating a MAC PDU containing the AI ​​data based on the request.

[0241] In some example embodiments, the request also indicates the period of AI data transmission.

[0242] In some example embodiments, the request also indicates the type of header to be used in the MAC PDU containing AI data.

[0243] In some example embodiments, the first device further includes a component for receiving an RRC message from the second device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0244] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0245] In some example embodiments, the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data.

[0246] In some example embodiments, the first apparatus further includes: a component for determining a bitmap, the bitmap indicating whether AI data is included in a MAC PDU containing AI data and the type of AI data; and a component for generating a header of a MAC PDU containing AI data based on the bitmap.

[0247] In some example embodiments, the first apparatus further includes: a component for determining a unique number or unique eLCID of the type of AI data assigned to the MAC PDU containing AI data; and a component for generating a header of the MAC PDU containing AI data based on the unique number or unique eLCID.

[0248] In some example embodiments, the first apparatus further includes a component for generating a header of a MAC PDU containing AI data based on a fixed LCID assigned to AI data of a predetermined type.

[0249] In some example embodiments, the first device further includes a component for transmitting capability information to the second device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0250] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0251] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0252] In some example embodiments, a second device capable of performing any of method 1000 (e.g., Figure 1 The second device 120 may include a component for performing a corresponding operation of method 1000. This component can be implemented in any suitable form. For example, the component can be implemented in a circuit system or a software module. The second device can be implemented as... Figure 1 The second device 120 may be included therein.

[0253] In some example embodiments, the second device includes: components for transmitting a request for AI data to the first device; components for receiving a TB from the first device, the TB including at least a MAC PDU containing AI data; and components for obtaining AI data from the MAC PDU containing AI data based at least on the header of the MAC PDU containing AI data.

[0254] In some example embodiments, the second device further includes a component for transmitting a request via MAC-CE to the first device that at least instructs the first device to provide a specific type of AI data.

[0255] In some example embodiments, the request also indicates the period of AI data transmission.

[0256] In some example embodiments, the request also indicates the type of header to be used for the MAC PDU containing AI data.

[0257] In some example embodiments, the second device further includes a component for transmitting an RRC message to the first device, the RRC message indicating that the first device collects AI data via a MAC PDU containing AI data.

[0258] In some example implementations, the RRC message also indicates the period of AI data transmission.

[0259] In some example embodiments, the header of the MAC PDU containing AI data indicates at least one of the following: the presence of AI data or the type of AI data.

[0260] In some example embodiments, the second apparatus further includes: components for determining the presence and type of AI data based on the header of the MAC PDU containing AI data; and components for acquiring AI data of that type based on the determination that the AI ​​data is present.

[0261] In some example embodiments, the second apparatus further includes: components for determining whether AI data is included in a MAC PDU containing AI data and the type of AI data based on a bitmap included in the header; and components for obtaining AI data associated with the type of AI data based on the determination that AI data is included in a MAC PDU containing AI data.

[0262] In some example embodiments, the second apparatus further includes: a component for determining the type of AI data contained in the MAC PDU containing AI data based on a unique number or unique eLCID assigned to the type of AI data contained in the MAC PDU containing AI data; and a component for acquiring the AI ​​data of that type.

[0263] In some example embodiments, the second apparatus further includes a component for acquiring AI data corresponding to a predetermined type based on a fixed LCID assigned to AI data of a predetermined type.

[0264] In some example embodiments, the second device further includes a component for receiving capability information from the first device, the capability information indicating that the first device supports transmitting AI data via a MAC PDU containing AI data.

[0265] In some example embodiments, the MAC PDU includes a MAC sub-PDU.

[0266] In some example embodiments, the first device includes a terminal device, and the second device includes a network device.

[0267] Figure 11 This is a simplified block diagram of a device 1100 suitable for implementing exemplary embodiments of the present disclosure. The device 1100 can be provided to implement a communication device, for example, such as... Figure 1 The first device 110 or the second device 120 shown. As shown, device 1100 includes one or more processors 1110, one or more memories 1120 coupled to processor 1110, and one or more communication modules 1140 coupled to processor 1110.

[0268] Communication module 1140 is used for bidirectional communication. Communication module 1140 has one or more communication interfaces to facilitate communication with one or more other modules or devices. The communication interface can represent any interface required for communication with other network elements. In some example embodiments, communication module 1140 may include at least one antenna.

[0269] Processor 1110 can be of any type suitable for a local technology network, and by way of non-limiting example, can include one or more of the following: general-purpose computer, special-purpose computer, microprocessor, digital signal processor (DSP), and processor based on a multi-core processor architecture. Device 1100 can have multiple processors, such as application-specific integrated circuit chips that are time-dependent on a clock synchronized with the main processor.

[0270] Memory 1120 may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, read-only memory (ROM) 1124, electrically programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disk (DVD), optical disc, laser disc, and other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, random access memory (RAM) 1122 and other volatile memories that do not persist during power outages.

[0271] Computer program 1130 includes computer-executable instructions that are executed by an associated processor 1110. The instructions of program 1130 may include instructions for performing operations / actions of some example embodiments of this disclosure. Program 1130 may be stored in memory, such as ROM 1124. Processor 1110 can perform any suitable actions and processes by loading program 1130 into RAM 1122.

[0272] Example embodiments of this disclosure can be implemented via program 1130, enabling device 1100 to execute reference... Figures 2 to 10 Any process discussed in this disclosure. Exemplary embodiments of this disclosure may also be implemented using hardware or a combination of software and hardware.

[0273] In some example embodiments, program 1130 may be tangibly contained in a computer-readable medium, which may be included in device 1100 (such as memory 1120) or other storage device accessible to device 1100. Device 1100 may load program 1130 from the computer-readable medium into RAM 1122 for execution. In some example embodiments, the computer-readable medium may include any type of non-transitory storage medium, such as ROM, EPROM, flash memory, hard disk, CD, DVD, etc. The term "non-transitory" as used herein refers to a limitation on the medium itself (i.e., tangible, not tactile), rather than a limitation on the persistence of data storage (e.g., RAM and ROM).

[0274] Figure 12 An example of a computer-readable medium 1200, which may be in the form of a CD, DVD, or other optical storage disc, is shown. A program 1130 is stored on the computer-readable medium 1200.

[0275] Generally, the various embodiments of this disclosure can be implemented using hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented using hardware, while others can be implemented using firmware or software that can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are illustrated and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein are non-limiting examples that can be implemented using hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0276] Some exemplary embodiments of this disclosure also provide at least one computer program product tangibly stored on a computer-readable medium, such as a non-transitory computer-readable medium. The computer program product includes computer-executable instructions, such as instructions included in a program module, which execute in a device on a target physical or virtual processor to perform any of the methods described above. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc., that perform a particular task or implement a particular abstract data type. In various embodiments, the functionality of a program module can be combined or split among program modules as needed. The machine-executable instructions of a program module can execute within a local or distributed device. In a distributed device, a program module can reside on both local and remote storage media.

[0277] Program code used to perform the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that, when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a stand-alone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0278] In the context of this disclosure, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, etc.

[0279] Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination of the foregoing. More specific examples of computer-readable storage media will include electrical connections having one or more wires, portable computer floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0280] Furthermore, although operations are described in a specific order, this should not be construed as requiring the operations to be performed in the specific order shown or sequentially, or to perform all of the shown operations to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure, but rather as descriptions of features that may be specific to particular embodiments. Unless explicitly stated otherwise, certain features described in the context of a single embodiment may also be implemented in combination in a single embodiment. Conversely, unless explicitly stated otherwise, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0281] Although this disclosure has been described in language specific to structural features and / or methodological actions, it should be understood that the disclosure as defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are disclosed as exemplary forms of implementing the claims.

Claims

1. A first device for communication, comprising: At least one processor; as well as At least one memory stores instructions that, when executed by the at least one processor, cause the first device to at least: Generate a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data, wherein the header of the MAC PDU containing the AI ​​data indicates at least one of the following: the presence of the AI ​​data or the type of the AI ​​data; Generate a transport block TB, the TB including at least the MAC PDU containing the AI ​​data; and The TB is transmitted to the second device.

2. The first device according to claim 1, wherein the first device is caused to perform at least one of the following: Determine a bitmap indicating whether the AI ​​data is included in the MAC PDU containing the AI ​​data and the type of the AI ​​data; and based on the bitmap, generate the header of the MAC PDU containing the AI ​​data. Determine a unique number or unique enhanced logical channel identifier (eLCID) of the type of AI data assigned to the AI ​​data included in the MAC PDU containing the AI ​​data; and generate the header of the MAC PDU containing the AI ​​data based on the unique number or the unique eLCID. Based on the Fixed Logical Channel Identifier (LCID) assigned to AI data of a predetermined type, the header of the MAC PDU containing the AI ​​data is generated; Determine the position of the MAC PDU containing the AI ​​data relative to at least one other MAC PDU that does not contain the AI ​​data; or Obtain a pattern for placing the MAC PDU containing the AI ​​data relative to the at least one other MAC PDU; and determine the location of the MAC PDU containing the AI ​​data in the TB based on the pattern.

3. The first device according to claim 2, wherein the mode includes at least one of the following: The location in the TB preceding the at least one additional MAC PDU. The location within the TB between the at least one additional MAC PDU, It is located in the TB after the at least one additional MAC PDU.

4. The first apparatus of claim 2, wherein the mode is obtained from a request or based on a default mode, the request instructing the first apparatus to provide a specific type of AI data via a Media Access Control-Control Element (MAC-CE).

5. The first device according to claim 1, wherein the first device is caused to perform at least one of the following: The capability information is transmitted to the second device, indicating that the first device supports transmitting the AI ​​data via the MAC PDU containing the AI ​​data. Receive a request from the second device via MAC-CE, at least instructing the first device to provide a specific type of AI data; and based on the request, generate the MAC PDU containing the AI ​​data, wherein the request also indicates the period of transmission of the AI ​​data; or The first device receives a Radio Resource Control (RRC) message from the second device, the RRC message instructing the first device to collect AI data via the MAC PDU containing the AI ​​data, and wherein the RRC message also indicates the period of transmission of the AI ​​data.

6. The first apparatus according to any one of claims 1 to 5, wherein the MAC PDU includes a MAC subprotocol data unit subPDU.

7. The first device according to any one of claims 1 to 5, wherein the first device includes a terminal device and the second device includes a network device.

8. A second means for communication, comprising: At least one processor; as well as At least one memory stores instructions that, when executed by the at least one processor, cause the second device to at least: The first device receives a transport block TB, the TB including at least a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data; as well as The AI ​​data is obtained from the MAC PDU containing the AI ​​data, at least based on the header of the MAC PDU containing the AI ​​data.

9. A method for communication, comprising: The first device generates a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data, wherein the header of the MAC PDU containing the AI ​​data indicates at least one of the following: the presence of the AI ​​data or the type of the AI ​​data; The TB is generated, the TB including at least the MAC PDU containing the AI ​​data; and The TB is transmitted to the second device.

10. A method for communication, comprising: The second device receives a transport block TB from the first device, the TB including at least a Media Access Control (MAC) Protocol Data Unit (PDU) containing artificial intelligence (AI) data; as well as The AI ​​data is obtained from the MAC PDU containing the AI ​​data, at least based on the header of the MAC PDU containing the AI ​​data.

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